{"id":10546,"date":"2022-06-28T10:52:33","date_gmt":"2022-06-28T14:52:33","guid":{"rendered":"http:\/\/149.4.100.129\/academics\/smns\/?page_id=10546"},"modified":"2022-06-28T12:47:44","modified_gmt":"2022-06-28T16:47:44","slug":"data-science-faq","status":"publish","type":"page","link":"https:\/\/www.qc.cuny.edu\/academics\/smns\/data-science-faq\/","title":{"rendered":"Data Science FAQ"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row column_structure=&#8221;1_2,1_2&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_image src=&#8221;https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-content\/uploads\/sites\/46\/2022\/06\/DataCollab.png&#8221; title_text=&#8221;DataCollab&#8221; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; url=&#8221;https:\/\/www.qc.cuny.edu\/academics\/smns\/data-science\/&#8221; sticky_enabled=&#8221;0&#8243;][\/et_pb_image][\/et_pb_column][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; text_text_color=&#8221;#A9A9A9&#8243; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;]<\/p>\n<div class=\"navigation-wrapper\">\n<div id=\"main-superfish-wrapper\" class=\"menu-wrapper\">\n<p style=\"text-align: right\"><span style=\"color: #808080\"><strong><\/strong><\/span><\/p>\n<p style=\"text-align: right\"><span style=\"color: #808080\"><strong><\/strong><\/span><\/p>\n<p style=\"text-align: right\"><span style=\"color: #808080\"><strong><\/strong><\/span><\/p>\n<p style=\"text-align: right\"><span style=\"color: #808080\"><strong><\/strong><\/span><\/p>\n<p style=\"text-align: right\"><span style=\"color: #808080\"><strong><span style=\"color: #808080\"><a href=\"https:\/\/www.qc.cuny.edu\/academics\/smns\/data-science\/\" style=\"color: #808080\">HOME<\/a><\/span> \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 <a href=\"https:\/\/www.qc.cuny.edu\/academics\/smns\/data-science-courses\/\"><span style=\"color: #808080\">COURSES<\/span><\/a><\/strong><\/span><\/p>\n<\/div>\n<\/div>\n<div class=\"clear\"><\/div>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_image src=&#8221;https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-content\/uploads\/sites\/46\/2022\/06\/datasc_11.jpeg&#8221; title_text=&#8221;datasc_11&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; border_color_all=&#8221;#A9A9A9&#8243; border_style_all=&#8221;ridge&#8221; box_shadow_style=&#8221;preset4&#8243; global_colors_info=&#8221;{}&#8221;][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;-54px||||false|false&#8221; custom_padding=&#8221;0px||||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; text_font_size=&#8221;16px&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"font-size: large\"><strong>FREQUENTLY ASKED QUESTIONS<\/strong><\/span><span style=\"font-size: medium\"><\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; custom_margin=&#8221;-115px||||false|false&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_accordion _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; border_color_bottom=&#8221;#A9A9A9&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;What is data?&#8221; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; border_color_bottom=&#8221;#A9A9A9&#8243; global_colors_info=&#8221;{}&#8221; toggle_level=&#8221;h3&#8243; toggle_font=&#8221;|700|||||||&#8221; open=&#8221;on&#8221;]<\/p>\n<div class=\"toggle-box-content\">\n<div class=\"toggle-box-content\">While the terms \u2018data\u2019 and \u2018information\u2019 are often used interchangeably, in the context of computing, data refers to distinct pieces of digital information in its unprocessed or unorganized form. Because data are not easily interpreted, we rely on software and machines to help us process and interpret data. Data Science and Digital Humanities constitute the ways in which we use technology to help us glean insight from data.<\/div>\n<\/div>\n<p>[\/et_pb_accordion_item][et_pb_accordion_item title=&#8221;What is BIG data?&#8221; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; border_color_bottom=&#8221;#A9A9A9&#8243; global_colors_info=&#8221;{}&#8221; toggle_level=&#8221;h3&#8243; toggle_font=&#8221;|700|||||||&#8221; open=&#8221;off&#8221;]<\/p>\n<div class=\"toggle-box-content\">\n<div class=\"toggle-box-content\">\n<p>Big data refers to extremely large data sets that can be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.<\/p>\n<p>To learn about the history of digital data and how it got BIG, check out this dynamic <strong><a href=\"https:\/\/www.thinkful.com\/blog\/what-is-data-science\/\" target=\"_blank\" rel=\"noopener\">article from Thinkful<\/a><\/strong>.<\/p>\n<p>To learn about the concerns of Big Data and why data professions require ethical practitioners, check out this <strong><a href=\"https:\/\/www.wired.com\/story\/should-data-scientists-adhere-to-a-hippocratic-oath\/\" target=\"_blank\" rel=\"noopener\">article from WIRED<\/a><\/strong>.<\/p>\n<p><iframe title=\"What is Big Data and how does it work?\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/TzxmjbL-i4Y?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<\/div>\n<\/div>\n<p>[\/et_pb_accordion_item][et_pb_accordion_item title=&#8221;Why is data literacy important?&#8221; open=&#8221;off&#8221; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; border_color_bottom=&#8221;#A9A9A9&#8243; global_colors_info=&#8221;{}&#8221; toggle_level=&#8221;h3&#8243; toggle_font=&#8221;|700|||||||&#8221;]<\/p>\n<div class=\"toggle-box-content\">\n<div class=\"toggle-box-content\">\n<p>Data literacy is a term used to describe an individual\u2019s ability to read, understand, and utilize data in different ways. As data collection and sharing are embedded in almost every aspect of modern life, it is increasingly important to be data literate. Much like literacy as a general concept, data literacy focuses on the competencies involved in understanding data:<\/p>\n<ul>\n<li class=\"p1\"><strong>Basic data literacy<\/strong> involves being able to interpret and understand basic data visualizations, statistics, and infographics, and perhaps most importantly, being able to recognize when data is misrepresented or misused. Learn more<b>\u00a0<\/b><a href=\"https:\/\/www.youtube.com\/watch?v=DterNVQAkcY\"><span class=\"s1\"><b>here,<\/b><\/span><\/a>\u00a0<a href=\"https:\/\/www.youtube.com\/watch?v=B8ofWFx525s\"><span class=\"s1\"><b>here<\/b><\/span><\/a><b>,\u00a0<\/b>and <a href=\"https:\/\/www.youtube.com\/watch?v=y8yMlMBCQiQ#action=share\"><span class=\"s1\"><b>here<\/b><\/span><\/a>.<\/li>\n<li class=\"p1\"><strong>Intermediate data literacy<\/strong> involves proficiency in basic data tools and methods, including knowing when to use them (e.g., qualitative vs quantitative data, surveys vs interviews, tables vs line charts, etc.).<\/li>\n<li class=\"p1\"><strong>Advanced data literacy<\/strong> involves expertise in data tools and methods, which includes being able to think critically about the information yielded by data analysis and being able to communicate this information to an audience that lacks data literacy. To learn more about data literacy, check out this <a href=\"https:\/\/www.tableau.com\/about\/blog\/2018\/9\/data-literacy-critical-skill-21st-century-94221\" target=\"_blank\" rel=\"noopener\"><strong>article from Tableau<\/strong><\/a>.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><strong>The <a href=\"http:\/\/www.qcsociology.org\/data-analytics\/our-programs\/undergraduate-minor-in-data-analytics\/\" target=\"_blank\" rel=\"noopener\">Minor in Data Analytics<\/a> <\/strong>(20 credits, DATA-MIN) teaches students with little or no background how data are produced, captured, organized, analyzed and presented\u2014and how to perform these data analytics tasks themselves. Course Requirements:<\/p>\n<ul class=\"ul1\">\n<li class=\"li1\">DATA 235. Data and Society<\/li>\n<li class=\"li1\">DATA 205. Introductory Analytics<\/li>\n<li class=\"li1\">DATA 212W. Research Methods<\/li>\n<li class=\"li1\">DATA 306. Data Modeling<\/li>\n<li class=\"li1\">DATA 333. Data Processing, Management, and Visualization<\/li>\n<li class=\"li1\">DATA 334. Applied Research<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p class=\"p1\">For more information email <a href=\"mailto:datanalytics@qc.cuny.edu\"><span class=\"s2\"><strong>datanalytics@qc.cuny.edu<\/strong><\/span><\/a> or enroll directly into DATA 235, 205, or 212W and speak to the instructor about the Minor.<\/p>\n<\/div>\n<\/div>\n<p>[\/et_pb_accordion_item][et_pb_accordion_item title=&#8221;Which careers involve data art?&#8221; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; border_color_bottom=&#8221;#A9A9A9&#8243; global_colors_info=&#8221;{}&#8221; toggle_level=&#8221;h3&#8243; toggle_font=&#8221;|700|||||||&#8221; open=&#8221;off&#8221;]<\/p>\n<div class=\"toggle-box-content\">\n<p>Since we react to the aesthetics of visual information just as much as we react to the information itself, how we <em>present<\/em> information\u00a0is equally important \u2013 design and content go hand in hand. Data art is digital information presented in a way that is both aesthetically appealing and easily comprehensible. Every graph, every PowerPoint presentation, every website, and every email involves data art and design to some degree.<\/p>\n<p>Aside from working as an artist that uses\u00a0<strong><a href=\"https:\/\/medium.com\/@Infogram\/meet-6-artists-who-have-swept-data-art-into-the-digital-age-d5c5ae805bab\" target=\"_blank\" rel=\"noopener\">data as a medium<\/a><\/strong>, jobs involving data art generally involve being able to tell a story graphically. Such skills are sought after in data analysts, but some companies look to employ dedicated Data Visualization Designers\/Editors\/Developers. Careers involving data art can be found in:<\/p>\n<ul>\n<li><strong>News agencies:<\/strong> Develop infographics, such as maps, charts, and graphs, often as a data journalist.<\/li>\n<li><strong>Design studios:<\/strong> Create graphic designs and digital art.<\/li>\n<li><strong>Analytics departments:<\/strong> Make data easier to comprehend for decision-making by creating interactive data visualization dashboards.<\/li>\n<li><strong>Research labs:<\/strong> Find new ways to represent and visualize data, often at universities or data visualization companies.<\/li>\n<li><strong>Freelancing:<\/strong> Develop a compelling portfolio and advertise to businesses that require help with data visualization from time to time.<\/li>\n<\/ul>\n<div id=\"attachment_61\" style=\"width: 677px\" class=\"wp-caption alignnone\">\n<p><img decoding=\"async\" data-src=\"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-content\/uploads\/sites\/46\/2022\/06\/Apollo-by-Paul-Button-300x180.png\" alt=\"\" class=\"wp-image-10522 alignnone size-medium lazyload\" width=\"300\" height=\"180\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 300px; --smush-placeholder-aspect-ratio: 300\/180;\" \/><\/p>\n<p id=\"caption-attachment-61\" class=\"wp-caption-text\">Apollo Missions by Paul Button<\/p>\n<\/div>\n<\/div>\n<p>[\/et_pb_accordion_item][et_pb_accordion_item title=&#8221;Which careers involve data science?&#8221; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; border_color_bottom=&#8221;#A9A9A9&#8243; global_colors_info=&#8221;{}&#8221; toggle_level=&#8221;h3&#8243; toggle_font=&#8221;|700|||||||&#8221; open=&#8221;off&#8221;]<\/p>\n<div class=\"toggle-box-content\">\n<p>Data science is an interdisciplinary field that combines technical tools from quantitative disciplines to methods of inquiry in other disciplines.\u00a0Careers that involve data science generally involve being able to structure data, interpret data, effectively communicate information gleaned from data, and provide evidence-based recommendations and actionable insights. For this reason, knowledge of statistics and technical skills in statistical software (e.g. SPSS, SAS, R), scripting language (e.g. Python), and querying language (e.g. SQL) are key.<\/p>\n<p>These careers also emphasize creative problem solving, critical thinking, teamwork, communication, and asking interesting questions. Because data and digital technologies have become an integral part of nearly every sector, many professions now involve working with data, including jobs that allow you\u00a0to take on real-world problems\u00a0in education, government, health, energy, public safety, transportation, economic development, international development, and others. Examples include:<\/p>\n<ul>\n<li>Biostatistician<\/li>\n<li>Business Intelligence Specialist<\/li>\n<li>Cartographer<\/li>\n<li>Climatologist<\/li>\n<li>Computer Security Analyst<\/li>\n<li>Epidemiologist<\/li>\n<li>Financial Analyst<\/li>\n<li>Data Analyst<\/li>\n<li>Data Engineer<\/li>\n<li>Data Visualization Developer<\/li>\n<li>Data Journalist<\/li>\n<li>Database Manager<\/li>\n<li>Machine Learning Engineer<\/li>\n<li>Market Analyst<\/li>\n<li>Policy Analyst<\/li>\n<li>Research Coordinator<\/li>\n<li><a href=\"https:\/\/www.theatlantic.com\/sponsored\/ibm-how-technology-transforms\/blood-sweat-and-data\/164\/\" target=\"_blank\" rel=\"noopener\">Sports Analyst<\/a><\/li>\n<li>Social Network Analyst<\/li>\n<li>Survey Researcher<\/li>\n<\/ul>\n<p><iframe title=\"The Data Scientist - 60 Second Data Science\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/i2jwZcWicSY?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<p class=\"p1\">A data scientist\u2019s job is to arrange undefined sets of data for analysis. This can include writing algorithms or building statistical models. If you have interests in coding and analysis and like the idea of supporting evidence-based decision-making, our <b>Mathematics: Data Science and Statistics (BA) <\/b>may be for you.<\/p>\n<p class=\"p1\">The <a href=\"https:\/\/www.queensgssp.com\/programs\/bachelor-of-data-science\" target=\"_blank\" rel=\"noopener\"><b>Data Science and Statistics BA<\/b><\/a> provides a strong background in statistics and data analysis, with an emphasis on cross-disciplinary and computational courses that are especially tailored for a career in data science. The required coursework focuses on core mathematics (multivariable calculus, linear algebra, logic &amp; proofs, real analysis), statistics from many perspectives (options in math, sociology, biology), high-level theoretical statistics (graduate level probability, statistical inference, bayesian modeling, time series), computer programming fundamentals (C++ \/ Java), statistical modeling (two econometrics courses, a special writing in the major course focused on prediction models), and practical modeling (via Excel w\/VBA, a formal writing in the major course with R). Electives allow students to specialize in different emerging areas of data science such as data engineering, predictive analytics or visualization.<\/p>\n<p class=\"p1\">Graduates of the program are prepared for careers in data science and analytics in any field, as well as for continued study at the graduate level. For more information, email <a href=\"mailto:math@qc.cuny.edu\"><span class=\"s1\">math@qc.cuny.edu<\/span><\/a> or call 718-997-5800 to make an appointment to speak with an advisor.<\/p>\n<\/div>\n<p>[\/et_pb_accordion_item][et_pb_accordion_item title=&#8221;Who can I speak to if I want to learn more?&#8221; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; border_color_bottom=&#8221;#A9A9A9&#8243; global_colors_info=&#8221;{}&#8221; toggle_level=&#8221;h3&#8243; toggle_font=&#8221;|700|||||||&#8221; open=&#8221;off&#8221;]<\/p>\n<div class=\"toggle-box-content\">\n<p>For general questions about QC\u2019s INTERDISCIPLINARY DATA COLLABORATIVE, contact <strong><a href=\"mailto:daniel.weinstein@qc.cuny.edu\">Dr. Dan Weinstein<\/a><\/strong>.<\/p>\n<p>For questions about <em>data and how it relates to a specific field<\/em>, make an appointment with a faculty member below:<\/p>\n<ul>\n<li><strong>Accounting &amp; Information Systems:<\/strong> <a href=\"mailto:fang.sun@qc.cuny.edu\">Fang Sun<\/a>, <a href=\"mailto:steven.solieri@qc.cuny.edu\">Steven Solieri<\/a>, Accounting<\/li>\n<li><strong>Advertising:<\/strong> <a href=\"mailto:mara.einstein@qc.cuny.edu\">Mara Einstein<\/a>, Media Studies<\/li>\n<li><strong>Anthropology: <\/strong><a href=\"mailto:megan.victor@qc.cuny.edu\">Megan Victor<\/a>, Archaeology<\/li>\n<li><strong>Art &amp; Design:<\/strong> <a href=\"mailto:danne.woo@qc.cuny.edu\">Danne Woo,<\/a> Art<\/li>\n<li><strong>Computational Linguistics, Social Network Analytics:<\/strong> <a href=\"mailto:charles.gomez@qc.cuny.edu\">Charles Gomez<\/a>, Sociology<\/li>\n<li><strong>Computational Modeling:<\/strong>\u00a0<a href=\"mailto:jeff.beeler@qc.cuny.edu\">Jeff Beeler<\/a>, Biology\u00a0<\/li>\n<li><strong>Computer Science: <\/strong><a href=\"mailto:tbrown@qc.cuny.edu\">Ted Brown<\/a>, <a href=\"mailto:mayank.goswami@qc.cuny.edu\">Mayank Goswami<\/a>, <a href=\"mailto:Alexander.Ryba@qc.cuny.edu\">Alex Ryba<\/a><\/li>\n<li><strong>Health Care Analytics: <\/strong><a href=\"mailto:dana.weinberg@qc.cuny.edu\">Dana Weinberg<\/a>,\u00a0Sociology<\/li>\n<li><strong>Data Analytics: <\/strong><a href=\"mailto:charles.gomez@qc.cuny.edu\">Charles Gomez<\/a>, <a href=\"mailto:elena.vesselinov@qc.cuny.edu\">Elena Vesselinov<\/a>, <a href=\"mailto:hongwei.xu@qc.cuny.edu\">Hongwei Xu<\/a>, <a href=\"mailto:shige.song@qc.cuny.edu\">Shige Song<\/a>, Sociology<\/li>\n<li><strong>Data Science and Statistics:<\/strong> <a href=\"mailto:chanusa@qc.cuny.edu\">Chris Hanusa<\/a>, <a href=\"mailto:adam.kepelner@qc.cuny.edu\">Adam Kapelner<\/a>, Mathematics<\/li>\n<li><strong>Demographic Research:<\/strong>\u00a0<a href=\"mailto:holly.reed@qc.cuny.edu\">Holly Reed<\/a>, Sociology<\/li>\n<li><strong>Digital Humanities:<\/strong>\u00a0<a href=\"mailto:douglas.rushkoff@qc.cuny.edu\">Douglas Rushkoff,<\/a> Media Studies; <a href=\"mailto:kevin.ferguson@qc.cuny.edu\">Kevin Ferguson<\/a>, English<\/li>\n<li><strong>Digital Preservation and Storytelling:<\/strong> <a href=\"mailto:james.lowry@qc.cuny.edu\" target=\"_blank\" rel=\"noopener\">James Lowry<\/a>, Archival Technologies Lab, GSLIS<\/li>\n<li><strong>Digitization and Storage:<\/strong> <a href=\"mailto:jonathan.thayer@qc.cuny.edu\">Jonathan Thayer<\/a>,\u00a0<a href=\"mailto:Jose.Sanchez@qc.cuny.edu\">Jose Sanchez<\/a>, Library and Information Science<\/li>\n<li><strong>Epidemiology:<\/strong> <a href=\"mailto:charles.turner@qc.cuny.edu\">Chalres Turner<\/a>, Sociology<\/li>\n<li><strong>Economics:<\/strong> <a href=\"mailto:Francisco.Ortega@qc.cuny.edu\">Francisco Ortega<\/a>, <a href=\"mailto:suleyman.taspinar@qc.cuny.edu\">Suleyman Taspinar<\/a>, Economics<\/li>\n<li><strong>Longitudinal Data Modeling:\u00a0<\/strong><a href=\"mailto:yoko.nomura@qc.cuny.edu\">Yoko Nomura<\/a>, Psychology<\/li>\n<li><strong>Mathematics and Data Science: <\/strong><a href=\"mailto:alan.sultan@qc.cuny.edu\">Alan Sultan<\/a>, <a href=\"mailto:adam.kapelner@qc.cuny.edu\">Adam Kapelner<\/a>, <a href=\"mailto:christopher.hanusa@qc.cuny.edu\">Chris Hanusa<\/a>, Mathematics<\/li>\n<li><strong>Mathematical Modeling:<\/strong> <a href=\"mailto:larry.liebovitch@qc.cuny.edu\">Larry Liebovitch<\/a>, Physics<\/li>\n<li><strong>Market Research:\u00a0<\/strong><a href=\"mailto:joseph.cohen@qc.cuny.edu\">Joseph Cohen<\/a>, Sociology<\/li>\n<li><strong>Neural Imaging and Analysis: <\/strong><a href=\"mailto:jin.fan@qc.cuny.edu\">Jin Fan<\/a>, Neuroscience<\/li>\n<li><strong>Sociology:<\/strong> <a href=\"mailto:Yinxian.Zhang@qc.cuny.edu\">Yinxian Zhang<\/a>, <a href=\"mailto:dana.weinberg@qc.cuny.edu\">Dana Weinberg<\/a>, <a href=\"mailto:amy.hsin@qc.cuny.edu\">Amy Hsin<\/a>, Sociology<\/li>\n<\/ul>\n<\/div>\n<p>[\/et_pb_accordion_item][et_pb_accordion_item title=&#8221;Where can I learn more about data and its applications?&#8221; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; border_color_bottom=&#8221;#A9A9A9&#8243; global_colors_info=&#8221;{}&#8221; toggle_level=&#8221;h3&#8243; toggle_font=&#8221;|700|||||||&#8221; open=&#8221;off&#8221;]<\/p>\n<div class=\"gdl-page-float-left\">\n<div class=\"gdl-page-item\">\n<div class=\"sixteen columns mt0\">\n<div class=\"gdl-page-content\">\n<div class=\"et_builder clearfix\">\n<div class=\"et_lb_module et_lb_text_block et_lb_first\">\n<ul class=\"gdl-toggle-box\">\n<li class=\"gdl-divider\">\n<div class=\"toggle-box-content\">\n<p class=\"p1\">You can explore some of the applications of data through the videos below.<\/p>\n<h5><strong>Data Science<\/strong><\/h5>\n<p><iframe title=\"The Data Scientist - 60 Second Data Science\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/i2jwZcWicSY?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<h5><strong>Data Visualization<\/strong><\/h5>\n<p><iframe title=\"The Art of Data Visualization | Off Book | PBS Digital Studios\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/AdSZJzb-aX8?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<h5><strong>Data Journalism<\/strong><\/h5>\n<p><iframe title=\"The Age of Insight: Telling Stories with Data\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/TA_tNh0LMEs?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<h5><strong>Storytelling with data<\/strong><\/h5>\n<p><iframe title=\"The best stats you&#039;ve ever seen | Hans Rosling\" width=\"1080\" height=\"810\" data-src=\"https:\/\/www.youtube.com\/embed\/hVimVzgtD6w?start=105&#038;feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<h5><strong>Digital Arts and Design<\/strong><\/h5>\n<p><iframe title=\"John Maeda: How art, technology and design inform creative leaders\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/WAuDCOl9qrk?start=185&#038;feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<h5><strong>Data and Applied Statistics<\/strong><\/h5>\n<p><iframe title=\"How statistics can be misleading - Mark Liddell\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/sxYrzzy3cq8?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<h5><strong>Data and Public Policy<\/strong><\/h5>\n<p><iframe title=\"Data Science in a Public Context\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/jwSv0TI1vIQ?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<h5><strong>Introduction to Big Data<\/strong><\/h5>\n<p><iframe title=\"Big Data - Tim Smith\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/j-0cUmUyb-Y?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<h5><strong>Thinking Critically about Big Data and Data Policy<\/strong><\/h5>\n<p><iframe title=\"Data makes you smart, but it doesn&#039;t make you wise | Timothy Snyder | Big Think\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/DterNVQAkcY?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<p><iframe title=\"The Dangers of Big Data\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/y8yMlMBCQiQ?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<p><iframe title=\"Introduction to Data Ethics - Brent Mittelstadt\" width=\"1080\" height=\"608\" data-src=\"https:\/\/www.youtube.com\/embed\/qVo9oApl4Rs?feature=oembed\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" data-load-mode=\"1\"><\/iframe><\/p>\n<\/div>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>HOME \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 COURSES FREQUENTLY ASKED QUESTIONS While the terms \u2018data\u2019 and \u2018information\u2019 are often used interchangeably, in the context of computing, data refers to distinct pieces of digital information in its unprocessed or unorganized form. Because data are not easily interpreted, we rely on software and machines to help us [&hellip;]<\/p>\n","protected":false},"author":87,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"page_category":[],"wf_page_folders":[281],"class_list":["post-10546","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-json\/wp\/v2\/pages\/10546","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-json\/wp\/v2\/users\/87"}],"replies":[{"embeddable":true,"href":"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-json\/wp\/v2\/comments?post=10546"}],"version-history":[{"count":0,"href":"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-json\/wp\/v2\/pages\/10546\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-json\/wp\/v2\/media?parent=10546"}],"wp:term":[{"taxonomy":"page_category","embeddable":true,"href":"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-json\/wp\/v2\/page_category?post=10546"},{"taxonomy":"wf_page_folders","embeddable":true,"href":"https:\/\/www.qc.cuny.edu\/academics\/smns\/wp-json\/wp\/v2\/wf_page_folders?post=10546"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}